Energy-Efficient Tree-Based EEG Artifact Detection

Presenting the ePoster in Glasgow


In the context of epilepsy monitoring, EEG artifacts are often mistaken for seizures due to their morphological similarity in both amplitude and frequency, making seizure detection systems susceptible to higher false alarm rates. In this work we present the implementation of an artifact detection algorithm based on a minimal number of EEG channels on a parallel ultra-low-power (PULP) embedded platform.

14, Jul, 2022
Scottish Event Campus
Exhibition Way, Glasgow, Glasgow G3 8YW
Thorir Mar Ingolfsson
Thorir Mar Ingolfsson
PhD Student in Robust and Practical Machine Learning

My research interests include TinyML, Hardware Aware NAS and application of ML for bio-signals.